A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

doi:10.1088/1674-1056/19/5/058701

Abstract In this paper, the ensemble empirical mode decomposition (EEMD) is applied to analyse accelerometer signals collected during human normal walking. First, the self-adaptive feature of EEMD is utilised to decompose the accelerometer signals, thus sifting out several intrinsic mode functions (IMFs) at disparate scales. Then, gait series can be extracted through peak detection from the eigen IMF that best represents gait rhythmicity. Compared with the method based on the empirical mode decomposition (EMD), the EEMD-based method has following advantages: it remarkably improves the detection rate of peak values hidden in the original accelerometer signal, even when the signal is severely contaminated by the intermittent noises; this method effectively prevents the phenomenon of mode mixing found in the process of EMD. And a reasonable selection of parameters for the stop-filtering criteria can improve the calculation speed of the EEMD-based method. Meanwhile, the endpoint effect can be suppressed by using the auto regressive and moving average model to extend a short-time series in dual directions. The results suggest that EEMD is a powerful tool for extraction of gait rhythmicity and it also provides valuable clues for extracting eigen rhythm of other physiological signals.

Keywords: ensemble empirical mode decomposition gait series peak detection intrinsic mode functions

Received: 18 August 2009
Revised: 08 September 2009
Accepted manuscript online:

PACS:	87.85.Ng	(Biological signal processing)
	87.19.rs	(Movement)
	87.19.L-	(Neuroscience)
	02.30.Uu	(Integral transforms)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 60501003 and 60701002).

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Fu Mao-Jing(符懋敬), Zhuang Jian-Jun(庄建军), Hou Feng-Zhen(侯凤贞), Zhan Qing-Bo(展庆波),Shao Yi(邵毅), and Ning Xin-Bao(宁新宝) A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition 2010 Chin. Phys. B 19 058701

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A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

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